Hyper-parameters optimization is modeled as a sequential decision making problem in [1]} and RL is used to find the optimal hyper-parameters. In this framework, the agent learns to explore the space of hyper-parameters of a supervised learning algorithm and the final parameters minimize error on the validation set. This method is naturally for supervised learning and works based on training and validation datasets. However, the general idea can be extended to RL algorithms. In the MDP modeling of the hyper-parameters optimization problem, the state of the environment is defined as the meta-features of an input dataset plus the history of evaluated hyper-parameters together with their performance, the action is a value for each hyper-parameter and the reward is the performance of ML algorithm on the input dataset with the selected hyper-parameters. Upon modeling the problem as MDP, deep Q network algorithm [2]} is used for learning the parameters of an LSTM system and the obtained policy works as a decision maker to determine optimal hyper-parameters for each dataset.
